Multivariate residual-based health index for human health monitoring

ABSTRACT

Ambulatory or in-hospital monitoring of patients is provided with early warning and prioritization, enabling proactive intervention and amelioration of both costs and risks of health care. Multivariate physiological parameters are estimated by empirical model to remove normal variation. Residuals are tested using a multivariate probability density function to provide a multivariate health index for prioritizing medical effort.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. §119(e)to U.S. Provisional Patent Application Ser. No. 61/295,072 filed Jan.14, 2010, which is fully incorporated herein for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under award numberIIP-0810751 awarded by the National Science Foundation. The Governmenthas certain rights in this invention.

BACKGROUND OF INVENTION

1. Field of the Invention

The present invention relates generally to the field of human healthmonitoring, and more particularly to the use of multivariate models foranalysis of measurements of biological parameters to provideresidual-based assessment of human health indicators.

2. Brief Description of the Related Art

Medicine has for centuries been practiced as a reactive, crisis-drivenprocess. Unfortunately, it remains largely so to this day. Chronicdiseases represent a disproportionate share of the crushing economiccost of healthcare, much of which could be avoided by early warning ofdeterioration. Current healthcare practices are episodic andreactionary, with little visibility into patient health outside thecontrolled setting of the clinic or hospital. However the medical artsare only now beginning to explore out-patient telemetry from wearabledevices, and there is virtually no answer to who is going to watch allthis data, or how it will be analyzed to provide early warning with alow false alert rate. Moreover, out-patient telemetry poses considerablechallenges due to ambulatory motion artifact and normal physiologyvariation in the course of daily activities not usually dealt with whena patient is sedated and supine in a hospital bed.

Other industries (nuclear, aviation, refining, computer systems) have inrecent years adopted advanced intelligent algorithms for conditionmonitoring, that accommodate normal variation and dynamics exhibited inthe sensor data collected from a target system, and differentiate itfrom subtle early warning signs of deterioration. One kind of machinelearning technique, Similarity-Based Modeling (“SBM”) technology, hasproven successful in many applications including those mentioned above.SBM is a nonparametric data driven modeling technique which learnsnormal behavior from multivariate data from a complex system, anddistinguishes it from the onset of adverse behavior in a monitoredsystem.

Visibility into health issues with SBM is contingent on the availabilityof multivariate data. Continuous telemetry from a wearable sensingdevice with multiple sensors could provide such data. However, existingdevices are data-poor, in most instances univariate, and are primarilyaimed at very narrow health related issue, e.g. glucose monitoring fordiabetics, or blood pressure for hypertension. The devices are usuallynot meant for continuous monitoring, and any analysis performed is doneusing gross population statistics, i.e. not personalized to theindividual. Further, current commercial telehealth devices are noteasily wearable, and do not take advantage of the latest mobiletechnologies.

There is a need to make multivariate continuous data available foranalysis, whether from a wearable device on an out-patient basis or frombedside equipment in a hospital, so that machine learning technologylike the aforementioned SBM can be applied to automate early detectionof incipient changes indicating the health of the patient is potentiallysubject to deterioration. Because medical staff is commonly overworkedand short on time to spend deeply studying analytical results for eachpatient, especially where large populations of at-home patients may beinvolved, an important issue is how to summarize the results of suchmachine learning techniques in a simple metric for actionability.

SUMMARY OF THE INVENTION

An end-to-end human health monitoring solution is disclosed, comprisedof a wearable wireless sensing device that continuously collects vitalsigns sensor data and transmits it (in real-time or in periodic bursts)to a base-station computer (or cell-phone/PDA) for preprocessing. Thepreprocessed data is then sent to a server over the web for analysisusing a kernel-based machine learning analytical method tailored forhuman monitoring, such as SBM. The SBM technology is trained to bespecific to each individual's normal vital signs characteristics. Due tothe variation in vital signs data from human to human, this capabilityis crucial for any human monitoring system to be effective.

The server can be remotely located from the patient. The analysisperformed at the server with SBM or other related kernel-based methodworks by generating estimates of the vital signs (i.e., physiologicaldata) that have been determined from the sensor data. These estimatesrepresent what a trained SBM model can determine as the closestallowable normal physiological data that corresponds to the monitoreddata. The estimates made of the physiological data are differenced withthe actual, monitored physiological data to generate residuals,representing the differences between the expected values according tothe trained model, and what has been measured by the wearable sensingdevice. These residuals form the basis for further analysis thatprovides early detection of subtle warning of health problems, whichwould likely be missed using conventional medical methods of comparingvital signs to demographically acceptable ranges (e.g., population-basedstandards for blood pressure).

Residuals for normal physiology (physiology as previously modeled) aredifferent from residuals for physiology that is beginning to deviatefrom normal, and can be statistically distinguished. The furthercomputerized analysis of the residuals comprises one or more of thesteps of: determining a likelihood that the residuals derived for anygiven multivariate input observation of monitored data arerepresentative of a pattern of residuals characteristic of normalphysiology, based on a “mixture of Gaussians” density estimation;generating a multivariate health index based on that likelihood as alogarithm of the inverse of the likelihood; applying a threshold to theindex thus generated to render a decision whether the inputted vitalsigns are characteristic of normal physiological behavior; and combininga series of such decisions to provide an early indication of deviationfrom normal of the physiological health of a patient. The multivariatehealth index advantageously summarizes the residual analysis frommultiple variables into a single index for the management of prioritizedlists of patients.

The health monitoring solution can also be applied to multivariatephysiological parameters obtained in a hospital from bedside monitors.An SBM model of typical human physiology can be used to make estimatesand residuals for patients in the hospital, particularly those at riskfor developing complications such as sepsis or pneumonia, andparticularly patients who are sedated and/or ventilated and not able toexpress discomfort or feelings of incipient illness. Bedside data feedsamenable to the health monitoring solution include electrocardiographs,pulse oximeters, ventilator data, arterial and venous pressures measuredby noninvasive means or by catheters, and the like. Such data can bestreamed to a server for the hospital ward, or to offsite servers formonitoring multiple hospital facilities, and decision support can berendered by application of SBM to these data streams and displayed tohealthcare workers for prioritizing patient treatment.

The analytics of the present invention can be performed on genericcomputing platforms specially configured by software. Data collectedfrom sensors on the patient can be wirelessly transmitted to anambulatory or portable device, e.g., via Bluetooth or other extremelylocal radio protocol. The portable device can be a cell phone carried bythe patient, a “personal digital assistant”, PDA, or the like, or aportable computing device moved with a patient in the hospital bed. Thisdevice may receive raw sensor signals and perform the aforementionedpreprocessing to extract vital sign “features” (physiological data) fromthe sensor signals, for example a heart rate from an EKG/ECG signal; ormay receive already-preprocessed features extracted by sensormicroprocessing facilities from raw sensor signals. The resultingphysiological “feature” data can be analyzed with SBM either on thedevice (the cell phone or PDA) or on a computer/server to which suchphysiological data is transferred. The computer can be a home computercollocated with the patient, or can be a remote server at an analyticsdata center. The transfer of data from the device can be by means ofcabled offload or by wireless retransmission.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asthe preferred mode of use, further objectives and advantages thereof, isbest understood by reference to the following detailed description ofthe embodiments in conjunction with the accompanying drawings, wherein:

FIG. 1 is a block diagram showing a general arrangement according to oneembodiment;

FIG. 2 shows an example of sensor placement on a human;

FIG. 3 shows an example chart of raw physiological waveforms or signals;

FIG. 4 shows a signal amplitude chart of photoplethysmography componentsused to determine a feature related to SpO2 (blood oxygen saturation),which may be understood to represent the light components picked up by aphotosensor stacked additively;

FIG. 5 is a multi-chart example plot showing in the top four plots rawphysiologically-related signals, and in the bottom five plots therelated feature data derived there from;

FIG. 6 is a plot of an exemplary physiological feature time seriesshowing perturbations of that time series used in accuracy androbustness calculations;

FIG. 7A is one of a pair of related plots of a multivariate health indexand has been derived merely for raw feature data showing an index forunperturbed data and for perturbed data;

FIG. 7B is a multivariate health index plot derived for residual datagenerated from kernel-based models of feature data showing and index forunperturbed data and for perturbed data; and

FIG. 8 is a block diagram showing an alternative embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

There are a plethora of chronic ailments and illnesses of which apatient may suffer, but for which the patient cannot be keptindefinitely in a hospital. A patient may have heart failure, chronicobstructive pulmonary disease, renal failure, diabetes, early stagedementia and other conditions, which can devolve from a stable, managedstate into an emergency health risk with little apparent warning. It isdesirable to detect such devolution early because medical interventionat the early stage can prevent the emergency, avoid costs, preventdisease progression, and improve outcomes.

Even patients in the hospital under care of medical staff can developcomplications that are best detected early. Patients on ventilatorssuffer a high rate of developing pneumonia. Infection and sepsis canoccur due to hospital-acquired cross-contaminant infections or frompost-surgical complications. Conventional bedside monitoring typicallyemploys thresholds on vital signs to alert staff of patientdeterioration, but these conventional alerting methods are coarse,either suffering a high false alert issue and rapidly disappearing intothe ignored background noise, or catching the deterioration later thanis desired.

Unlike the majority of monitoring approaches used in the healthcareindustry today, SBM is a multivariate approach that takes advantage ofthe interrelationships between vital signs signals (e.g., heart rate(HR), blood oxygen saturation (SpO2), Respiration Rate, Blood Pressure).Such an approach is critical for the analysis of physiology in thepresence of normal variation, that is, variation of physiological datadue to normal changes in physiology responsive to metabolic needs,activity, environment, diurnal cycles and the like. Over the course of aday, a typical human exhibits a wide range of heart rates, respirationrates, blood pressures, blood oxygen levels and so on. In contrast to asedated patient in a hospital setting, ambulatory conditions areexceptionally plagued by such variation, and as a result there has beenlittle traditional medical monitoring of humans in their normal lives athome except in extremely controlled circumstances. Even in a sedatedcondition in the hospital, normal patient physiology still exhibitssubstantial variation. Such variation hides early changes inphysiological parameters that evidence incipient deterioration ofhealth. Conventional alerts placed on single parameters cannot see suchchanges against the background of normal variation until such changesbecome extreme. For example, a threshold placed on heart rate cannot beset to trigger an alert merely because the heart rate rises by 10 beatsper minute, because this may readily occur in normal physiology. But ifthe threshold is set to 160 bpm, a patient's condition may already havedeteriorated substantially by the time the threshold is exceeded.

In addition, much of the sensing technology being developed today isburdened by the necessity to provide an exactly calibrated reading ofthe vital sign of interest. In contrast, SBM requires only relativeproxies of the vital sign of interest, thereby avoiding the problem ofattaining absolute calibration of a physiological parameter in order tomeasure health. This is because the detection of incipient healthproblems is based on relative changes between all biosignals inaggregate, not on exceedances from population-based vital sign ranges.

SBM achieves these advantages by embodying normal variation in a model(“learning”). This model is then used to generate multivariate estimatesof the learned physiological parameters when presented with amultivariate measurement of those parameters. These estimates representthe closest possible set of values for normally varying physiology, tothe presented (measured) values. The estimates are differenced with thepresented values to yield residuals. Analysis is advantageously shiftedfrom testing raw physiological values which are plagued by normalvariation, to testing residuals which represent differences beyondmerely normal variation. In effect, SBM removes normal variation bysubtracting the estimated behavior from the measured behavior, leavingjust deviations.

As described herein, the residuals are analyzed using a multivariatedensity estimation technique. According to this novel approach, themultidimensional distribution of residual vectors (vectors of dimensionn where n is the number of physiological parameters for which estimateswere differenced with actual measured values) for data representative ofthe patient's normal physiology is used to form a multivariate densityestimator. The density estimator is a Gaussian mixture model, and isused to determine the likelihood that any new input residual vector(i.e., from newly monitored data) is part of the same distribution. Thislikelihood obtained from the multidimensional density estimatoreffectively consolidates the behaviors of the individual residuals foreach of the physiological parameters, into one overall index that can beused to summarize patient priority. This likelihood can be used as amultivariate health index (MHI), and can be subsequently tested with anumber of persistence rules to assess patient priority over a timeseries of observations of the multiple physiological parameters beingmonitored.

Advantageously, this MHI analysis of model-generated residuals providesearlier warning of incipient health issues when compared to conventionalmedical univariate thresholds on raw physiological data, and whencompared to multivariate density estimates of raw physiological data.

Turning to FIG. 1, the overall approach can be appreciated. In step 105,multiple biosignals are acquired from sensors on or in the patient.Examples of appropriate biosignals include electrocardiographs (ECG),thoracic bioimpedance (bio-Z), photoplethysmographs (PPG), temperaturedifferentials, systolic or diastolic blood pressures,accelerometer-measured motion, piezoelectric signals of respiratoryactivity, and instant airflow measurements from respiration, to name afew. In step 110, these biosignals are used to derive physiologicalfeature data. A variety of physiological features can be derived fromsuch biosignals, with a commonly understood example being heart ratedetermined from landmarks of the ECG signal. Similarly, thoracicbioimpedance can yield respiratory rate and depth; PPG can yield pulsetransit time (when cross referenced to the ECG) and the blood oxygensaturation, and so on. A variety of physiological features are known inthe art, and the application of SBM in subsequent steps readilycontemplates the use of new features as well, because the method isagnostic to the signals used (as long as the model is trained on thesame kind of data) so long as they interrelate through the feedbackloops and control mechanisms of human physiology. In optional step 115,the derived features can be supplemented with otherphysiologically-relevant data, that is, data that impacts thephysiological behavior or response of the monitored human. An example isFiO2, the fraction of oxygen in inspired air, which can be increasedover room air with the use of supplementing oxygen. In step 120, akernel-based model such as SBM that has been trained on normal variationof these same physiological features generates estimates of an inputobservation of the features. Typically, an estimate is made for allelements in an input vector comprised of the collection of physiologicalparameters sampled contemporaneously. In step 125, the residuals aregenerated between those features measured and corresponding estimates ofthose features, in the instant monitored observation. Optionally,threshold tests can be applied in a univariate manner or in amultivariate pattern-matching manner to the residuals in step 130. Inparallel with that option, the residuals are processed in step 135 by amixture model developed from “normal” residuals, and a multivariatehealth index is determined for the input observation in step 140. ThisMHI is an index of the likelihood that the residuals from the inputobservation belong to the multivariate distribution of the mixturemodel. The MHI can also be tested with a threshold to determine if thelikelihood is insufficient such that the input observation evidencesdeviations not characteristic of normal physiology. In step 145,persistence rules can be applied to a time series of MHI determinationsto further test observation-over-observation in time the persistence ofthreshold exceedances, providing greater confidence that a deviation isoccurring in the patient's health, and is not merely a transientphenomenon in the data. In a step 150, the alerts from the MHI and itstest, along with any previous tests on individual residuals or residualpatterns, is managed for prioritization of patient care via a userinterface. Alert management can facilitate user-initiated annotationsinto a medical record system relating to the alerts of “dismissal”,“elevation” or “monitor” and other actions.

The biosignals of step 105 can be acquired from typical hospital vitalsigns equipment such as bedside monitors and ventilators, from mobilevital signs monitors, implanted devices such as implantable cardioverterdefibrillators and pacemakers with instrumentation, and from wearableambulatory monitors. Whatever data source device is used, it mustcollect biosignals capable of providing multiple related physiologicalvariables or features contemporaneously and at least periodically, ifnot continuously. In one form, a patient uses a non-invasive ambulatorysensing device or has an implantable device to acquire biosignals on atleast a semi-continuously basis throughout the patient's normal dailyactivities. Data acquired by a sensing device can be offloaded fromdevice memory on a periodic basis and thereafter processed on acomputer; or can be continuously transmitted by cellular network orWiFi, to be processed either continuously or in batch-mode by areceiving computer or server. The physiological features can even beanalyzed using the residual-based method on a smartphone or PDA, carriedby the patient, since the computing requirements of the analyticalprocess are well within the capabilities of modern mobile devices. Then,resulting alerts or health status conditions can be reported locally onthe mobile device, and can also be uploaded to a central server to beshared with medical practitioners.

One non-invasive wearable sensing device that can be used with thepresent invention is designed to acquire data from 4 types of signals:ECG, red and infrared (IR) photoplethysmograph (PPG), bioimpedance, anda 3-axis accelerometer. These sensors provide a rich waveform set fromwhich physiologic features can be extracted. The extracted features (asopposed to the raw waveform data) are what ultimately drive theSBM-based human health monitoring approach. The device can be designedto record relevant biosignals for local storage, e.g., on an onboardmicroSD card; or for transmission via a built-in Bluetooth radio to acell phone or PDA carried by the patient. The device can be designed tohave a USB Mini-B connector that can be used to supply power to thedevice when recharging its battery, and that provides a mechanism forhigh-speed communication with a PC for periodically off-loading data, ifraw real-time sensor data are stored on a micro-SD card of the device.The device may use a microprocessor selected from the well known TexasInstruments MSP430 line, ideal given its low power consumptioncharacteristics, built-in ADC, DAC, timers, and multiple serialperipheral interfaces (SPI/UART/I2C). The Bluetooth interface can beprovided via a BlueCore 3 Plug-n-Go IC, a 96-pin BGA module from CSR,Inc., with minimal external component requirements, and a 2.4 GHz chipantenna.

A number of sensing interfaces can be used to provide data for thepresent invention. The electrocardiogram (ECG) can be implemented byusing a two-stage analog high pass filter (HPF), followed by aradio-frequency interference (RFI) filter and a micro-powerinstrumentation amp. It is crucial in an ambulatory mode to employ anRFI filter in front of this high gain differential amplifier. Withoutit, a phenomenon called RF rectification can occur in the differentialamplifier IC. Once an RF signal becomes rectified inside the IC, itresults in a DC offset error at the output and no amount of low passfiltering can remove the error. As the RFI changes over time the DCoffset changes as well resulting in an ECG signal that is highlysusceptible to artifacts. Two pickup electrodes can be used to acquirethe signal, for example on either side of the chest. The ECG istypically sampled at 12 bits and 256 Hz by the microprocessor.

A bioimpedance measurement can be made by using a dedicated 12-bitimpedance converter network analyzer IC (Analog Devices AD5933) inconjunction with a voltage to current stage and a programmable gaininstrumentation amplifier. An electrode placed under the left armpit canbe used to inject 425 μA of current at 50 kHz to a ground electrodefound on the opposite side of the torso. The same electrodes used topickup the ECG signal can be used to pick up the 50 KHz signal through a5 KHz HPF and an RFI filter. The difference in voltage is proportionalto body's impedance through the relationship V=IR. The AD5933 IC iscapable of measuring the complex impedance of the signal.

The PPG signal can be acquired by controlling a pair of LEDs (Red andInfrared) via a current limiting H-Bridge for light generation. Theunabsorbed light is measured using a reverse-biased PID photodetectorconnected to a transimpedance amplifier for initial gain. The measuredsignal is then fed to a second stage differential amplifier along with aDC-offset value generated in firmware from the output of themicroprocessor's DAC. The DC-offset value is meant to keep the signalwithin the rails of the differential amplifier so that the signal gaincan be maximized. The output of the second stage amplifier is preferablythen oversampled by a factor of 8 at 16384 Hz (for a final sampling rateof 256 Hz) after a waiting period of 488 μS after the LEDs have changedstates. The oversampling is applied to increase the signal-to-noiseratios of the PPG signals, which are highly susceptible to noise.

Accelerometer data can be generated by a LIS302DL MEMS digitalaccelerometer at 400 Hz (8 bits per axis). The digital readings arepreferably read by the microprocessor at a rate of 100 Hz.

The acquired data can be placed into two buffers: one that is flushedout to the file system (micro-SD), and one that is fed to the BluetoothIC for transmission. Each value is preceded with a single byte ID foridentification, and periodic “sync” blocks are inserted into theBluetooth stream to aid in data alignment. Each packet of data consistsof the ID byte, followed by two bytes containing the sample value.Periodic 32-bit timestamps are also transmitted by utilizing two packetsto represent the high and low words of a 32-bit seconds counter.

In one form, a subject is outfitted with four electrodes and one pulseoximetry sensor. Two types of electrodes can be used, carbon-rubbernon-adhesive electrodes and carbon-rubber adhesive electrodes, althoughother commercially available electrodes are readily contemplated for usein the embodiment. The electrodes are placed on the body as shown inFIG. 2: (A) corresponds to the Bioimpedance current source electrode,(C) is the +ECG electrode, (F) is the −ECG electrode, and (H) is theanalog ground electrode (AGND). The ECG leads are also used tosimultaneously pick up the bioimpedance response signal. The device canbe worn by either being placed in a stretchable chest strap with thenon-adhesive electrodes attached to the inside of the strap via Velcro,or it is placed in a pouch worn around the neck with leads running tothe adhesive electrodes. The PPG signal is acquired via a disposableNelicor reflective pulse oximetry sensor affixed to the forehead andconnected to the device. A typical example of the signals captured bythe wearable sensing device described above from a human subject isshown in FIG. 3. The signals are: (A) ECG, (B) x-axis accelerometer, (C)infrared photoplethysmograph (PPG), (D) real component of bioimpedance,and (E) imaginary component of bioimpedance. Not shown are the y and zaxis accelerometer signals, and the red PPG signal which are allcaptured as well.

Turning now to physiological feature generation, the raw data collectedfrom the wearable device is not directly analyzed with SBM. Instead aset of physiological features are derived from the raw waveform data.These derived features are what provide the insight into the status ofhuman cardiopulmonary control system and in turn the overall health ofan individual. According to one example, several features from twocategories can be used, cardiac derived and respiratory derived. Thecardiac derived features are heart rate (HR), pulse transit time (PTT)and the Red absorption to IR absorption PPG ratio (or Q). In oneexample, the HR feature can be obtained directly by measuring theinterval between consecutive QRS peaks in the ECG signal. The peaks aredetected using a multi-step procedure. First a digital HPF is applied tothe ECG signal. Then the filtered signal is split into 10 second datawindows that are de-trended to remove a straight line fit to the data.Next, within each window, the 98th percentile is calculated and thelocations of all samples above the 98th percentile are found. Allsamples found reside on a set of local peaks within the 10 secondwindow. The last step is to find the sample location of the maximumvalue for each of the local peaks within the window. These locations arethe individual QRS peaks in the ECG waveform. Then the HR rate is simplythe reciprocal of the time interval between each heart beat.

PTT is the delay time between the QRS peak and PPG pulse peak. Thisfeature is known to be inversely proportional to blood pressure. Tocalculate it, the robustness of the ECG QRS peak detection algorithm isexploited with first principles. Since it is known that a transit timeof more than 250 ms is unlikely in a human, 250 ms windows starting fromthe QRS peak location for each heart beat can be used to search for thecorresponding PPG peak. The maximum value within the window is the PPGpeak. This is done for both the red and IR PPG signals. Because the PPGsignals tend to be naturally noisy, before the peaks are located, thePPG signals are first digitally filtered using a median filter (toremove spiking) followed by a band-pass filter with lower and uppercutoff frequencies of 0.5 Hz and 5 Hz respectively.

The Q feature is the ratio of the blood absorption of red light toinfrared light. Q is inversely known to be proportional to SpO2 (bloodoxygen saturation). Calculating Q is more complicated due to the analogand digital signal processing that takes place before the raw PPG dataare acquired. With reference to FIG. 4, Q is calculated as follows. Thebasic equation for Q is given by

$\begin{matrix}{Q = \frac{\left( {{RED}_{A\; C}/{RED}_{D\; C}} \right)}{\left( {{IR}_{\;{A\; C}}/{IR}_{D\; C}} \right)}} & (1)\end{matrix}$

Here RED_(AC) (IR_(AC)) is the amount of red (infrared) light absorbedby the blood and REDDC (IR_(DC)) is the amount of red (infrared) lightabsorbed by the surrounding tissue. The PPG implementation comprises anLED driving stage, a PID photodiode with a transimpedance amplifier, anda second gain stage which subtracts out a DC offset (RED OUTPUTOFFSET inthe FIG. 4) and adds additional gain. Some level of background light isdetected by the sensor, and needs to be subtracted from the measuredsignal as well (OFF SIGNAL+OFF OUPUTOFFSET). The RED DC TRACK parameteris the lower envelop of the actual acquired signal. Then Q can be givenby the following equations (shown for red only).RED_(AC)=αRED′_(AC)  (2)RED_(AC)=α(RED_(DCTRACK))+β(RED_(OUTPUTOFFSET))−β(OFF_(OUTPUTOFFSET))−α(OFF_(SIGNAL))  (3)Here RED′_(AC) is the peak-to-peak value of the actual acquired PPGsignal, and α and β are scaling factors that are function of the analogto digital converters.

There are two respiratory derived features that can be used in theembodiment, respiration rate (RR) and tidal volume (TV) (or depth ofbreath). Both are calculated from the bioimpedance signal. The deviceacquires the real and imaginary parts of the bioimpedance separately.These are combined to form the magnitude which is used for extracting RRand TV. Bioimpedance is highly susceptible to motion artifacts. Musclemovement and organ movement change the impedance of the human bodycausing undesired variation in the acquired signal. At the same time thesignal is noisy and somewhat aperiodic in nature with respect tobreathing. Because of these factors one method to obtain reasonableresults for extracting RR and TV is a spectral-based approach. Thebioimpedance signal is first bandpass filtered with a narrow banddigital filter with lower and upper cutoff frequencies of 0.133 Hz and 1Hz (corresponding to a RR range of 8 to 60 breaths per minute). Next, asliding window Discrete Fourier Transform (DFT) is applied to thefiltered data with overlap to produce feature values every 20 seconds.The RR rate feature corresponds to the frequency at which the maximumvalue of the magnitude of the DFT occurs in each window. To reduce edgeeffects each window of data is multiplied with a window function thatsuppresses the end points to zero before the DFT is calculated. TV isdefined to be the value of the magnitude of the DFT at the RR frequency,and quantitatively relates to true tidal volume but is not a directlycalibrated measure of tidal volume.

In one form, two last steps are taken to finalize the feature generationprocess. First, in a noise filtering step that removes spikes andsmoothes the feature data at the same time, a moving window trimmed meanfilter is applied with 50% window overlap. The default window size is 40seconds and with an overlap of 50% the resulting filtered features occurat a rate of 1 sample every 20 seconds. The second step is to align allthe feature data in time so that they can be analyzed with SBM. This isachieved by interpolating all of the filtered features at the same timepoints using a shape-preserving piecewise cubic interpolator. An exampleof the filtered features is shown in FIG. 5 along with some of the rawsignals: (A) ECG, (B) y-axis accelerometer, (C) red PPG, (D)bioimpedance magnitude, (E) respiration rate, (F) tidal volume, (G)heart rate, (H) pulse transit time, and (I) red to infrared ratio. Dataregion 505 occurred while the subject held his breath as is evident bytidal volume (F) going to zero. During the same period the red to IR PPGratio (I) starts to increase indicating that O2 saturation is lowering.Region 510 occurred while the subject was walking briskly around. Afterabout 45 seconds into the walk his respiration rate, tidal volume andheart rate increase ((E), (F) and (G) respectfully). Pulse transit timedrops (H), indicating an increase in blood pressure, while the PPG ratio(I) begins to slowly climb again, indicating lower O2 saturation.Finally region 515 represents the subject running up and down astaircase three times with short rests in between. As expected, similarbehavior to that of region 510 is seen.

Invariably sensor noise, artifacts due to sensor movement and otherunexpected interference contaminate random time periods of the acquiredsensor data. Including tainted data in an SBM model can potentiallydegrade model performance. SBM is purely data driven and learnsnormality from the training data. If the training data is contaminatedwith non-health related artifacts the model's representation of normalwill be undesirably broadened. This generally affects its sensitivity inpredicting the onset of anomalous behavior.

To deal with sensor noise a number of digital filtering techniques canbe applied to either the raw data or to the calculated featuresthemselves. These include the techniques of median filtering, InfiniteImpulse Response (IIR) filters and Finite Impulse Response (FIR)filters).

According to one approach, a strategy for detecting artifacts in the rawsensor data is based on a number of components. First, the first orderdifference of each axis of the accelerometer data is monitored for timeswhen the absolute value of the difference is above a predefinedthreshold. These times indicate when sudden movements have occurred.Generally, these sudden movements result in transient behavior in thesensor data, most notably in the PPG data and bioimpedance data. Thedata from all sensors are then ignored from the first indication ofsudden movement until 10 seconds after the difference signals fallsbelow the threshold again. This approach works well for detectingtransients but does not detect sensor problems. The second componentcombines heuristic rules with first principles rules to detect sensorand/or feature generation errors. The set of rules is summarized below:

-   -   1. If TV<T_(tv) (a threshold constant) then RR is unreliable and        is not used. Calculating RR is based on extracting the maximum        spectral component of the bioimpedance signal within a narrow        band and if TV is below T_(tv) the person is not breathing, or        is breathing so shallowly that the maximum component is        meaningless; it's just the maximum noise component in the        frequency band during this state.    -   2. If HR>200 or Q (PPG Red to IR ratio)>T_(Q) (a threshold        constant), ignore the calculated feature value. A value of HR        above 200 is well above the normal HR for a human so anything        above 200 is likely an error. Similarly, Q, a proxy for SpO2, is        only realistic in a certain range; however unlike HR the range        varies from person to person due to sensor placement and the        physical characteristics of the skin. So a unique T_(Q) is        preferably calculated for each individual.    -   3. If the PTT variance is greater than the HR variance by more        than threshold constant T_(var), then ignore the feature data.        This means that the pulsatile peaks of the PPG signals are not        being identified correctly indicating that the PPG sensor is        physically out of place or is being overcome by noise.

Turning now to the process for estimating observations in order to beable to obtain residuals, a number of different kernel-basedmultivariate estimator methods may be used. What is generally intendedby the term “kernel-based” is a multivariate estimator that operateswith a library of exemplary observations (the learned data) on an inputobservation using a kernel function for comparisons. The kernel functiongenerally yields a scalar value (a “similarity”) on a comparison of theinput observation to an exemplary observation from the library. Thescalar similarity can then be used in generating an estimate as aweighted sum of at least some of the exemplars. For example, usingNadaraya-Watson kernel regression, the kernel function is used togenerate estimates according to:

$\begin{matrix}{y_{est} = {\frac{\sum\limits_{i = 1}^{L}{y_{i}^{out}{K\left( {x_{new},x_{i}^{i\; n}} \right)}}}{\sum\limits_{i = 1}^{L}{K\left( {x_{new},x_{i}^{i\; n}} \right)}}\left( {{Inferential}\mspace{14mu}{form}} \right)}} & (4) \\{x_{est} = {\frac{\sum\limits_{i = 1}^{L}{x_{i}{K\left( {x_{new},x_{i}} \right)}}}{\sum\limits_{i = 1}^{L}{K\left( {x_{new},x_{i}} \right)}}\left( {{Autoassociative}\mspace{14mu}{form}} \right)}} & (5)\end{matrix}$where x_(new) is the input multivariate observation of physiologicalfeatures, x_(i) are the exemplary multivariate observations ofphysiological features, x_(est) are the estimated multivariateobservations, and K is the kernel function. In the inferential case,exemplars comprise a portion x_(i) comprising some of the physiologicalfeatures, and a portion Y_(i) comprising the remaining features, x_(new)has just the features in x_(i), and Y_(est) is the inferential estimateof those Y_(i) features. In the autoassociative case, all features areincluded in x_(new), x_(i) and in the x_(est) together—all estimates arealso in the input.

The kernel function, by one approach, provides a similarity scalarresult for the comparison of two idenfically-dimensioned observations,which:

-   -   1. Lies in a scalar range, the range being bounded at each end;    -   2. Has a value of one of the bounded ends, if the two vectors        are identical;    -   3. Changes monotonically over the scalar range; and    -   4. Has an absolute value that increases as the two vectors        approach being identical.        In one example, kernel functions may be selected from the        following forms:

$\begin{matrix}{{K_{h}\left( {x_{a},x_{b}} \right)} = {\mathbb{e}}^{- \frac{{{x_{a} - x_{b}}}^{2}}{h}}} & (6) \\{{K_{h}\left( {x_{a},x_{b}} \right)} = \left( {1 + \frac{{{x_{a} - x_{b}}}^{\lambda}}{h}} \right)^{- 1}} & (7) \\{{K_{h}\left( {x_{a},x_{b}} \right)} = {1 - \frac{{{x_{a} - x_{b}}}^{\lambda}}{h}}} & (8)\end{matrix}$where x_(a) and x_(b) are input observations (vectors). The vectordifference, or “norm”, of the two vectors is used; generally this is the2-norm, but could also be the 1-norm or p-norm. The parameter h isgenerally a constant that is often called the “bandwidth” of the kernel,and affects the size of the “field” over which each exemplar returns asignificant result. The power λ may also be used, but can be set equalto one. It is possible to employ a different h and λ for each exemplarx_(i). Preferably, when using kernels employing the vector difference ornorm, the measured data should first be normalized to a range of 0 to 1(or other selected range), e.g., by adding to or subtracting from allsensor values the value of the minimum reading of that sensor data set,and then dividing all results by the range for that sensor; ornormalized by converting the data to zero-centered mean data with astandard deviation set to one (or some other constant). Furthermore, akernel function according to the invention can also be defined in termsof the elements of the observations, that is, a similarity is determinedin each dimension of the vectors, and those individual elementalsimilarities are combined in some fashion to provide an overall vectorsimilarity. Typically, this may be as simple as averaging the elementalsimilarities for the kernel comparison of any two vectors x and y:

$\begin{matrix}{{K\left( {x,y} \right)} = {\frac{1}{L}{\sum\limits_{m = 1}^{L}{K\left( {x_{m},y_{m}} \right)}}}} & (9)\end{matrix}$

Then, elemental kernel functions that may be used according to theinvention include, without limitation:

$\begin{matrix}{{K_{h}\left( {x_{m},y_{m}} \right)} = {\mathbb{e}}^{\frac{- {{x_{m} - y_{m}}}^{2}}{h}}} & (10) \\{{K_{h}\left( {x_{m},y_{m}} \right)} = \left( {1 + \frac{{{x_{m} - y_{m}}}^{\lambda}}{h}} \right)^{- 1}} & (11) \\{{K_{h}\left( {x_{m},y_{m}} \right)} = {1 - \frac{{{x_{m} - y_{m}}}^{\lambda}}{h}}} & (12)\end{matrix}$

The bandwidth h may be selected in the case of elemental kernels such asthose shown above, to be some kind of measure of the expected range ofthe m^(th) parameter of the observation vectors. This could bedetermined, for example, by finding the difference between the maximumvalue and minimum value of a parameter across all exemplars.Alternatively, it can be set using domain knowledge irrespective of thedata present in the exemplars or reference vectors, e.g., by setting theexpected range of a heart rate parameter to be 40 to 180 beats persecond on the basis of reasonable physiological expectation, and thus hequals “140” for the m^(th) parameter in the model which is the heartrate.

According to one approach, Similarity-Based Modeling is used as thekernel-based multivariate estimator. Three types of SBM models can beused for human data analysis tasks: 1) a fixed SBM model, 2) a localizedSBM model that localizes using a bounding constraint, and 3) a localizedSBM model that localizes using a nearest neighbor approach. The fixedSBM modeling approach generates estimates using the equation below.

$\begin{matrix}{{{\hat{x}}_{i\; n}(t)} = \frac{{D\left( {D^{T} \otimes D} \right)}^{- 1}\left( {D^{T} \otimes {x_{i\; n}(t)}} \right)}{\sum{\left( {D^{T} \otimes D} \right)^{- 1}\left( {D^{T} \otimes {x_{i\; n}(t)}} \right)}}} & (13)\end{matrix}$

Here, D is a static m-by-n matrix of data consisting of n training datavectors with m physiological features, pre-selected from normal dataduring a training phase. The kernel function K is present as a kerneloperator

whereby each column vector from the first operand (which can be amatrix, such as D is) is compared using one of the kernel functionsdescribed above, to each row vector of the second operand (which canalso be a matrix). The monitored input observation is here shown asx_(in)(t), and the autoassociative estimate is shown as {circumflex over(x)}_(in)(t). In contrast, localized SBM (LSBM) is given by thefollowing equation:

$\begin{matrix}{{{{\hat{x}}_{i\; n}(t)} = \frac{{D(t)}\left( {{D(t)}^{T} \otimes {D(t)}} \right)^{- 1}\left( {{D(t)}^{T} \otimes {x_{i\; n}(t)}} \right)}{\sum{\left( {{D(t)}^{T} \otimes {D(t)}} \right)^{- 1}\left( {{D(t)}^{T} \otimes {x_{i\; n}(t)}} \right)}}},{{D(t)} = \left\{ H \middle| {F\left( {H,{x_{i\; n}(t)}} \right)} \right\}}} & (14)\end{matrix}$

Although similar in form to the fixed SBM model, here the D matrix isredefined at each step in time using a localizing function F(•) based onthe current input vector x_(in)(t) and a normal data reference matrix H.Accordingly, matrix H contains a large set of exemplars of normal dataobservations, and function F selects a smaller set D using each inputobservation. By way of example, F can utilize a “nearest neighbor”approach to identify a set of exemplars to constitute D for the currentobservation as those exemplars that fall within a neighborhood of theinput observation in m-dimensional space, where m is the number offeatures. As another example, function F can compare the inputobservation to the exemplars for similarity using a kernel-basedcomparison, and select a preselected fraction of the most similarexemplars to constitute D. Other methods of localization arecontemplated by the invention, including selection on the basis of fewerthan all of the physiological features, and also selection on the basisof a distinct parameter not among the features, but associated with eachexemplar, such as an ambient condition measure.

Models used for estimation in the present invention are preferablyempirical models determined from data, in contrast to first-principlesmodels that relate parameters by deterministic equations. Therefore,instead of deriving a model, the model must be trained with empiricaldata. Training a model of physiology comprises gathering exemplaryobservations of the physiological parameters or features to be modeledand building a reference library of exemplars. These features can berange-normalized, or can be used in their native units of measurement incombination with an elementary kernel function, such as those shown inequations 10-12, that uses a bandwidth that is proportional to theexpected range in those native units of measure. In personalizedmodeling, observations are obtained of the features in question from thepatient who will be monitored, during conditions in which the patient isdeemed to be medically normal or medically stable. The patient need notbe in pristine health, as the method of the present invention looks forrelative change. The normal data preferably includes representation fromall manner of activity that is to be modeled, and need not be limited tohighly immobile, sedated or “steady state” conditions, unless those arethe only conditions that will be modeled. Exemplars are typically justobservations selected for inclusion in the reference library from thelarger set of available normal observations; exemplars can also bedetermined as computed “centers” of clustered normal data in thealternative.

Once a model is trained by constituting its reference library, andselecting the kernel function(s) that will serve as similarityoperations for estimate generation, the model can be used to generateestimates responsive to monitored input observations. With each inputobservation, an estimate of at least some of the physiological featuresis generated according to one of the embodiments of equations 4, 5, 13or 14 above. The estimated features are then differenced with themeasured values of those features in the instant observation to create aresidual for each such feature. Given that real-world signals haveinherent measurement noise and inherent system noise, and given thatempirical models will have some inherent inaccuracy, residuals willoccur not only for deviating data from deteriorating physiology, butalso for data from normal physiology. However the statistical characterof the residuals for normal data will be much better behaved than fordeviating data. A number of well known methods for testing raw data canbe applied to the residuals, including thresholds. A threshold can beapplied to a residual such that small variations are tolerated, bylarger values trigger an alert. Series of decisions on residuals forindividual physiological parameters can be the basis for rules relatingto the genuine existence of a persistent deviating health condition, forexample by counting the number of threshold exceedances in a window ofobservations. Rule patterns can be applied across residuals fordifferent physiological features, triggered only when the pattern ofdeviations in the residuals is identified. Generally, these decisionmethods applied to residuals are more sensitive and less prone to errorthan the same approaches applied to raw data, because normal variationhas been removed in the residuals by the differencing with the estimatedfeatures from the model. Essentially, SBM is removing the normalvariation in the actual data and leaving behind abnormal data in theform of residuals (normal as defined by the training data).

The performance of a model can be measured using a nonparametricperturbation-based approach that is particularly well suited forcomparing modeling techniques used for anomaly detection applications.The performance of a model is assessed using three metrics: 1)robustness, 2) spillover and 3) error. The robustness metric is ameasurement of the likelihood that a model will follow (or over-fit) aperturbation introduced into the data. With reference to FIG. 6, tomeasure robustness, first estimates for all of the variables in a modelare made based on a test data set containing normal data ({circumflexover (x)}₀ in FIG. 6). Next, a perturbation Δ is added to each variableone at a time in the model as shown (x_(Δ) in FIG. 6). Finally,estimates are generated for each of the perturbed variables ({circumflexover (x)}_(Δ) in FIG. 6). The robustness metric for each variable in amodel is then given by the following equation:

$\begin{matrix}{{Robustness} = \frac{{mean}\left( {{{\hat{x}}_{0} - {\hat{x}}_{\Delta}}} \right)}{\Delta}} & (15)\end{matrix}$

Here, perfect robustness is achieved when Robustness is equal to 0, thatis, when the unperturbed and perturbed estimates are identical. A largervalue indicates more over-fitting and hence less model robustness.

The spillover metric measures the relative amount that variables in amodel deviate from normality when another variable is perturbed. Incontrast to robustness, spillover measures the robustness on all othervariables when one variable is perturbed. The spillover measurement foreach variable is calculated using a similar calculation, which is givenby

$\begin{matrix}{{Spillover}_{j} = {\frac{1}{M - 1}{\sum\limits_{{i = 1},{i \neq j}}^{M}\frac{{mean}\left( {{{\hat{x}}_{i\; 0} - {\hat{x}}_{i|\Delta_{j}}}} \right)}{\Delta_{i}}}}} & (16)\end{matrix}$where {circumflex over (x)}_(i0) is the estimate for variable i when novariables are perturbed, is the estimate of variable i when variable jis perturbed by Δ_(j), and Δi is the perturbation amount used whenvariable i is itself perturbed.

Finally, the error metric is simply the root mean squared error of thedifference between the actual value and its estimate divided by thestandard deviation of the actual value, or equivalently the residual RMSdivided by the actual value standard deviation:

$\begin{matrix}{{Error} = {\frac{{rms}\left( {x - \hat{x}} \right)}{\sigma_{x}} \equiv \frac{{rms}({residual})}{\sigma_{x}}}} & (17)\end{matrix}$

The equations listed above define the metrics for each variable in amodel. In each case, a smaller value is better. The overall performancemetrics for a model are calculated by averaging the results for eachvariable in each case.

Turning to one form of residual testing, a multivariate densityestimation approach can be applied to the residual data. Theapproximated densities in the normal behavior of the data are used todetermine the likelihood (in the form of a multivariate health index(MHI)) that a new data point is part of the normal behaviordistribution. The density estimates are calculated using anon-parametric kernel estimator with a Gaussian kernel. The estimator isshown in the equation below. The resulting density function isessentially a mixture of N individual multivariate Gaussian functionseach centered at x_(i):

$\begin{matrix}{{\hat{f}(x)} = {\frac{1}{{N\left( {2\pi} \right)}^{d/2}h^{d}}{\sum\limits_{i = 1}^{N}{\exp\left\lbrack {{- \frac{1}{2}}\frac{{x - x_{i}}}{h^{2}}} \right\rbrack}}}} & (18)\end{matrix}$

where N is the number of training vectors, h is a bandwidth parameter, dis the dimensionality of the vectors, and {circumflex over (f)}(x) is ascalar likelihood. Importantly, the x and x_(i) here are notmultivariate observations of physiological features, but are insteadmultivariate residual observations derived from the originalobservations by differencing with the estimates. Importantly also, thedensity “estimation” here is not the same as the estimation processdescribed above for estimating physiological feature values based onmeasured values; the “estimate” here is empirically mapping out aprobability distribution for residuals using the normal multivariateresidual exemplars, as a Gaussian mixture model. This estimateddistribution is then used to compute a likelihood that a newmultivariate residual from an input observation of physiologicalfeatures is a member of that distribution or not. The exemplars x_(i)can be selected from regions of normal data residuals generated by SBMusing test data that is deemed “normal” or representative of desired orstable physiological behavior. Before the density estimates are made,all residuals are scaled to have unit variance and zero mean, or atleast are scaled to have unit variance. The means and standarddeviations used for the scaling procedure are calculated from knownnormal data residuals. The multivariate health index (MHI) in one formis a function of {circumflex over (f)}(x) and is given by:

$\begin{matrix}{{{MHI}(x)} = {\log_{10}\left( \frac{1}{\hat{f}(x)} \right)}} & (19)\end{matrix}$Of course, the likelihood determined from equation 18 need not beconverted as in equation 19 in order to be useful, and equation 19 isused primarily to invert the signal trend (so that higher equates torising health risk). Tests may be applied directly to the result ofequation 18.

A comparison of the efficacy of applying the multivariate densityestimation approach to residuals is highlighted in FIGS. 7A-7B. Chart705 (FIG. 7A) shows a multivariate density estimation similar to thatdescribed above except applied to raw physiological feature data (theactual values of heart rate, respiration rate, etc.); while chart 710(FIG. 7B) shows the multivariate density estimation as described aboveapplied to residuals generated from a kernel-based model (SBM). MHIresults are shown for physiological data both unperturbed (normal) andwith an artificially-induced perturbation (abnormal). The perturbationwas introduced as a slow drift in a subset of ambulatory physiologicalfeatures from the start of the data, with a maximum drift achieved atthe end of the data. In both chart 705 and 710, the MHI computed for“normal” unperturbed data is shown as a solid line, and the MHI computedfor “abnormal” perturbed data is shown as a dotted line. A detectionthreshold (717, 720) was determined for each approach based onstatistics for a test set of normal data, where the statistics were forraw data in the case of chart 705 and for residuals in the case of chart710. A decision algorithm was further applied to the MHI to ascertain apersistent, reliable threshold exceedance alert, in this case xsuccessive MHI threshold exceedances yields an alert decision. Thedecision can be latched until a series of y successive values for MHIare observed below the threshold, in which case the alert is removed.Alternatively, an alert can be latched when there have been x thresholdexceedances in a window of m observations, and the alert removed whenthere have been y observations below the threshold in a window of bobservations. In each case, the vertical line (730, 735) indicates thepoint at which a decision was made that the data are not from the normalbehavior distribution and hence indicate an abnormal condition. As canbe seen, detection occurs about one-third of the way from the start ofthe simulated disturbance for the residual-driven MHI, whereas detectionusing raw data in combination with a multivariate density estimationdoes not occur until much later in the data. This is due to thecombination of a model of normalcy removing normal variation, with themultivariate density estimation of likelihood of normalcy applied toresiduals. This residual-based MHI method has the novel advantages ofproviding substantially earlier detection of an incipient pattern ofdeviation in health, and providing a single index of patient deviationto summarize individual residuals for the multiple physiologicalfeatures being monitored.

According to one approach, the system described herein can be deployedto provide predictive monitoring of patient health in an ambulatory,at-home environment, particularly for patients with chronic diseasesthat may deteriorate unpredictably. Multiple physiological features arederived from one or more biosignals and parameters captured from awearable or implanted device (or both), and transmitted to an analyticsdata center, where one or more servers are disposed to process thephysiological features using empirical, kernel-based models. The modelsare preferably personalized to the data from the patient captured duringperiods when the patient is considered to be in normal or acceptablystable health, to provide a model of normal physiology for the patient.Monitored data is estimated using the personalized model, and themonitored values are differenced with the estimated values of thephysiological parameters to yield residuals. The residuals are thenprocessed through one or more methods of analysis to yield alertsregarding the patient's health status. According to one technique, theresiduals can individually be tested with rules, such as thresholds.These thresholds can further be tested for persistence. Patterns ofresidual tests can be recognized to yield even more specific healthstatus information. According to another technique, the multivariateobservation of residuals can be examined for likelihood of belonging toa “normal” residual distribution using an empirical multivariateprobability density estimation, and this likelihood may then beconverted to a multivariate health index, typically as an inverse logvalue of the likelihood. The MHI provides an instant ranking of patienthealth status, and the MHI can be tested using a threshold, as well aspersistence rules, to yield alerts regarding patient health status. Allsuch analytics can be presented via a web-based or client-server-baseduser interface to medical practitioners, and in this way a largepopulation of patients can be monitored together by medical staff withimproved efficiency. All such monitored patients of a health careinstitution or practice group can be managed for early warning ofdeteriorating health at home, and the patients can be prioritized forspecific follow-up based on health status. Patients with earlyindications of health deterioration can be contacted to verifycompliance with medications, inquire about how the patient feels, andinvestigate recent patient behavior that may have exacerbated a chronicillness. Medical staff may advantageously avert a more costly healthemergency for the patient with efficient interventions includinginstructing the patient to make adjustments to medications, comply withmedications, or come in for an examination and preventativeintervention.

SBM can also be deployed with cross subject modeling, instead of anentirely personalized model. A model then comprises data from otherhuman subjects. Due to the person to person variation in feature data itis necessary to scale each subject's data. A generic cross populationmodel can be used as a temporary means for monitoring a human when nohistorical data are available for the individual as long as theindividual's feature data are properly scaled. The scaling can beaccomplished based on statistics calculated during a standardized set ofactivities when the monitoring device is first put on. The data acquiredduring the standard activities (which can comprise lying down, sitting,standing, walking and climbing stairs, for example) is typically scaledto a zero-mean, one-standard deviation range. The monitoring is not assensitive as it would be for a personalized model but it at leastprovides a minimal level of health monitoring while waiting to acquire asuitable set of data to generate a personalized model.

Turning to FIG. 8, another approach obtains residuals from referencedata representative of a known illness, malady or health deterioration,so that a multivariate probability density estimator can be determinedfor that health deterioration, in contrast to determining it for normalor stable health. Hence, one or more probability density estimators 810can be created in this way (including one for normal data), and appliedto multivariate residual observations 820 from monitored data 830.Likelihoods that the monitored residual observation belongs to each ofthe distributions can be compared in parallel in a decisioning step 840,and not only can deviation from normal be detected, but the nature ofthe health deterioration can be categorized. Likelihoods can simply bedisplayed to medical staff, or the likeliest scenario or the set ofscenarios with a sufficiently high likelihood can be indicated as theprobable state(s) of the patient in 840. In another approach todecisioning 840, the likelihoods or MHI values for each of a pluralityof maladies are normalized using test statistics generated from knownexamples of each such malady processed through model estimation andresidual generation, so that they can be expressed in terms of thetypical variance expected for residual vectors fitting each suchcategory. Then the normalized values are compared to determine whichcategory is in fact most likely represented by the current monitoreddata. Series of MHI or likelihood values for each malady category canalso be processed heuristically to rank categories, for example withmoving window averages or medians.

According to another form, patients in a hospital are monitored withmultivariate physiological parameters derived from sensors usingconventional bedside monitors, ventilators, and/or wearable or implanteddevices. Data is streamed via Ethernet network or WiFi to a centralstation/nursing station or to a hospital centralized data center,coupled to interfaces for medical staff real-time monitoring. Data isalso streamed via. Ethernet network or WiFi to analytics server(s) forprocessing using empirical, kernel-based models as described herein.Estimates are made of the physiological features, and residuals aregenerated; models may be generic instead of personalized, since nopersonal data may be available for a patient from a period when thatpatient was in acceptable physiological health. In such a case, a modelcan comprise data from other humans collected in similar hospitalconditions when the humans were in acceptable health. Such a model canfurther be tailored to the monitored patient on the basis of majorcontributors to normal physiological variation, such as body mass,gender, age, and medical condition (e.g., similar cardiac ejectionfraction or similar respiratory performance). Residuals are processed asdescribed above to generate MHI and/or rules-based decisions. Patienthealth status for all monitored patients in the ward or hospital or ICUcan be monitored by onsite medical staff or off-site medical staff toprovide early warning of developing health issues, such as infection,pneumonia, and sepsis.

With the advantage of early warning as provided by the invention, thehealth alerts of patients can be managed in a proactive manner, ratherthan being a crisis that must be immediately responded to. The userinterface provides for several levels of alert management: Alerts can bedismissed (investigation by medical staff shows the alert to beanomalous); alerts can be confirmed and elevated (investigation bymedical staff shows a definite health issue is present that needsintervention); and alerts can be marked for further follow-up andobservation (investigation shows close monitoring is warranted butimmediate intervention is not required or advised).

A system is provided for advanced warning of health problems, using awearable sensing device for capturing rich physiological data streamsfrom a human outside the hospital, in the daily routine of their homelife, providing high visibility into a patient's physiological statusoutside the reach of the physician's office or the hospital ward.Automated processing of this data using algorithms that remove thenormal variation present in ambulatory data, to provide robust and earlydetection of anomalies indicative of incipient health issues is noveland inventive. The potential for this combination of device plusalgorithm to revolutionize patient care is enormous, especially for thechronically ill patient population. This platform is exactly the kind oftool needed by physicians to improve patient outcomes, avoid unnecessarycosts, and greatly extend the leverage of the medical workforce.

It will be appreciated by those skilled in the art that modifications tothe foregoing preferred embodiments may be made in various aspects andas set forth with particularity in the appended claims. It is deemedthat the spirit and scope of the invention encompasses suchmodifications and alterations to the preferred embodiment as would beapparent to one of ordinary skill in the art and familiar with theteachings of the present application.

What is claimed is:
 1. A method for monitoring the health of a human,comprising: obtaining sensor data from a human; generating with aprogrammed microprocessor a plurality of features from said sensor data,characteristic of physiological health of said human; estimating with aprogrammed microprocessor values for said features characteristic ofnormal human physiology using a multivariate model, based on the valuesof said generated plurality of features; differencing with a programmedmicroprocessor the estimated values and the generated features toprovide a set of residuals for the features, wherein each residual isthe difference between the particular feature value expected accordingto said model, and the corresponding feature value generated from saidsensor data; and determining with a programmed microprocessor alikelihood that said set of residuals is representative of a pattern ofnormal residuals, by using a Gaussian mixture model based on a set ofnormal residual reference patterns to approximate a probabilitydistribution for normal residual patterns, and to compute saidlikelihood that said set of residuals belongs to the distribution,whereby said likelihood consolidates the behaviors of the individualresiduals for each of the features into one overall index; and applyingwith a programmed microprocessor a test to said likelihood to render adecision whether the generated features are characteristic of normalphysiological behavior to provide an early indication of deviation ofthe physiological health of said human from normal.
 2. A methodaccording to claim 1, wherein said step of applying a test comprisescomparing with a programmed microprocessor the logarithm of the inverseof said likelihood to a threshold.
 3. A method according to claim 1,further comprising the step of testing with a programmed microprocessora series of said rendered decisions for persistence of like decisions.4. A method according to claim 1, wherein said step of estimating valuesfurther comprises making a kernel-based comparison of a feature vector,comprising the values of said feature signals, to at least some of alibrary of exemplary vectors, each comprising values representative ofsaid feature signals in a known health state, in order to generate saidestimate as a linear combination of those exemplary vectors, weighted inrelation to said comparisons.
 5. A method according to claim 4, whereinsaid feature vector is compared to said exemplary vectors comprisingsaid library in order to select a subset of said exemplary vectors touse in said kernel-based comparison for generating said estimate.
 6. Amethod according to claim 4, wherein said estimate is generated as alinear combination of said exemplary vectors, weighted in relation tosaid comparisons according to:$x_{est} = \frac{\sum\limits_{i = 1}^{L}{x_{i}{K\left( {x_{new},x_{i}} \right)}}}{\sum\limits_{i = 1}^{L}{K\left( {x_{new},x_{i}} \right)}}$where x_(new) is said feature vector, x_(i) are said exemplary vectors,x_(est) is said estimate, and K is said kernel-based comparison.
 7. Amethod according to claim 4, wherein said estimate is generated as alinear combination of said exemplary vectors, weighted in relation tosaid comparisons according to:$x_{est} = \frac{{D\left( {D^{T} \otimes D} \right)}^{- 1}\left( {D^{T} \otimes x_{new}} \right)}{\sum{\left( {D^{T} \otimes D} \right)^{- 1}\left( {D^{T} \otimes x_{new}} \right)}}$where x_(new) is said feature vector, D is a matrix of at least some ofsaid exemplary vectors, x_(est) is said estimate, and {circumflex over(x)} is an operator for performing said kernel-based comparisons betweenmatrices.
 8. A method according to claim 4, wherein said kernel-basedcomparison is of the form:${K_{h}\left( {x_{a},x_{b}} \right)} \propto {\mathbb{e}}^{- \frac{{{x_{a} - x_{b}}}_{p}^{n}}{h}}$where x_(a) and x_(b) are vectors being compared, h is a constant, p isthe order of the norm, and n is a power to which the norm is raised, andK is the scalar result of the comparison.
 9. A method according to claim4, wherein said kernel-based comparison is of the form:${K_{h}\left( {x_{a},x_{b}} \right)} \propto \left( {1 + \frac{{{x_{a} - x_{b}}}_{p}^{n}}{h}} \right)^{- 1}$where x_(a) and x_(b) are vectors being compared, h is a constant, p isthe order of the norm, and n is a power to which the norm is raised, andK is the scalar result of the comparison.
 10. A method according toclaim 4, wherein said kernel-based comparison is of the form:${K_{h}\left( {x_{a},x_{b}} \right)} \propto {1 - \frac{{{x_{a} - x_{b}}}_{p}^{n}}{h}}$where x_(a) and x_(b) are vectors being compared, h is a constant, p isthe order of the norm, and n is a power to which the norm is raised, andK is the scalar result of the comparison.
 11. A method according toclaim 4, wherein said kernel-based comparison is of the form:${K\left( {x,y} \right)} \propto {\frac{1}{L}{\sum\limits_{m = 1}^{L}{\mathbb{e}}^{\frac{- {{x_{m} - y_{m}}}^{n}}{h_{m}}}}}$where x and y are vectors being compared, h_(m) are constants, m is thenumber of features, and n is a constant power, and K is the scalarresult of the comparison.
 12. A method according to claim 4, whereinsaid kernel-based comparison is of the form:${K\left( {x,y} \right)} \propto {\frac{1}{L}{\sum\limits_{m = 1}^{L}\left( {1 + \frac{{{x_{m} - y_{m}}}^{n}}{h_{m}}} \right)^{- 1}}}$where x and y are vectors being compared, h_(m) are constants, m is thenumber of features, and n is a constant power, and K is the scalarresult of the comparison.
 13. A method according to claim 4, whereinsaid kernel-based comparison is of the form:${K\left( {x,y} \right)} \propto {\frac{1}{L}{\sum\limits_{m = 1}^{L}\left( {1 - \frac{{{x_{m} - y_{m}}}^{n}}{h_{m}}} \right)}}$where x and y are vectors being compared, h_(m) are constants, m is thenumber of features, and n is a constant power, and K is the scalarresult of the comparison.
 14. A method according to claim 1, whereinsaid step of obtaining sensor data comprises making measurements ofsensors embedded inside the monitored human in connection with animplanted cardiac device.
 15. A method according to claim 1, whereinsaid step of obtaining sensor data comprises receiving wirelesstransmissions via extremely local radio protocol of measurements ofsensors attached to the monitored human.
 16. A method according to claim1, wherein said step of obtaining sensor data comprises receiving datafrom a ventilator.
 17. A method for monitoring the health of a human,comprising: obtaining sensor data from a human; generating with aprogrammed microprocessor a plurality of features from said sensor data,characteristic of physiological health of said human; estimating with aprogrammed microprocessor values for said features characteristic ofnormal human physiology using a multivariate model, based on the valuesof said generated plurality of features; differencing with a programmedmicroprocessor the estimated values and the generated features toprovide a set of residuals for the features, wherein each residual isthe difference between the particular feature value expected accordingto said model, and the corresponding feature value generated from saidsensor data; determining with a programmed microprocessor for each of aplurality of known health states, a likelihood that said set ofresiduals is representative of a pattern of residuals characteristic ofthat known health state, by using a Gaussian mixture model based on aset of residual reference patterns for the known health state toapproximate a probability distribution for residual patterns of thatknown health state, and to compute said likelihood that said set ofresiduals belongs to the distribution; and applying with a programmedmicroprocessor a test to the plurality of likelihoods, eachcorresponding to one of the known health states, to render a ranking ofwhich of said plurality of known health states the generated featuresare most characteristic of.
 18. A method according to claim 17, whereinsaid step of estimating values further comprises making a kernel-basedcomparison of a feature vector, comprising the values of said featuresignals, to at least some of a library of exemplary vectors, eachcomprising values representative of said feature signals in a knownhealth state, in order to generate said estimate as a linear combinationof those exemplary vectors, weighted in relation to said comparisons.19. A method according to claim 18, wherein said feature vector iscompared to said exemplary vectors comprising said library in order toselect a subset of said exemplary vectors to use in said kernel-basedcomparison for generating said estimate.
 20. A method according to claim18, wherein said estimate is generated as a linear combination of saidexemplary vectors, weighted in relation to said comparisons accordingto:$x_{est} = \frac{\sum\limits_{i = 1}^{L}{x_{i}{K\left( {x_{new},x_{i}} \right)}}}{\sum\limits_{i = 1}^{L}{K\left( {x_{new},x_{i}} \right)}}$where x_(new) is said feature vector, x_(i) are said exemplary vectors,x_(est) is said estimate, and K is said kernel-based comparison.
 21. Amethod according to claim 18, wherein said estimate is generated as alinear combination of said exemplary vectors, weighted in relation tosaid comparisons according to:$x_{est} = \frac{{D\left( {D^{T} \otimes D} \right)}^{- 1}\left( {D^{T} \otimes x_{new}} \right)}{\sum{\left( {D^{T} \otimes D} \right)^{- 1}\left( {D^{T} \otimes x_{new}} \right)}}$where x_(new) is said feature vector, D is a matrix of at least some ofsaid exemplary vectors, x_(est) is said estimate, and {circumflex over(x)} is an operator for performing said kernel-based comparisons betweenmatrices.
 22. A method according to claim 1, wherein the sensor data ofsaid receiving step comprises an electrocardiogram, a bioimpedance, anda photoplethysmogram for at least two wavelengths.
 23. A methodaccording to claim 22, wherein the plurality of features of saidgenerating step comprises a heart rate, a respiration rate, a pulsetransit time, and a ratio of absorption of the at least two wavelengths.24. A method according to claim 23, wherein the sensor data of saidreceiving step further comprises at least one accelerometer signal, andfurther comprising the step of identifying what times the accelerometersignal indicates motion artifact is likely present in the sensor dataand ignoring sensor data at those times.
 25. A method according to claim1, wherein said step of determining a likelihood further comprisesscaling said residuals for the generated features, using the means andstandard deviations calculated from known normal data residuals, andwherein said set of normal residual reference patterns are likewisescaled.
 26. A method according to claim 3, wherein said step of testinga series of rendered decisions comprises toggling on an alert latch whenthere have been a first selected minimum count of rendered decisions,within a first selected window of successive observations of thegenerated features, that the features are not characteristic of normalphysiological behavior.
 27. A method according to claim 26, wherein saidstep of a testing series of rendered decisions further comprises, oncethe alert latch is on, toggling off the alert latch when there have beena second selected minimum count of rendered decisions, within a secondselected window of successive observations of the generated features,that the features are characteristic of normal physiological behavior.28. A method according to claim 17, wherein the sensor data of saidreceiving step comprises an electrocardiogram, a bioimpedance, and aphotoplethysmogram for at least two wavelengths.
 29. A method accordingto claim 28, wherein the plurality of features of said generating stepcomprises a heart rate, a respiration rate, a pulse transit time, and aratio of absorption of the at least two wavelengths.
 30. A methodaccording to claim 29, wherein the sensor data of said receiving stepfurther comprises at least one accelerometer signal, and furthercomprising the step of identifying what times the accelerometer signalindicates motion artifact is likely present in the sensor data andignoring sensor data at those times.
 31. A method according to claim 17,wherein said step of determining, for each of a plurality of knownhealth states, a likelihood further comprises scaling said residuals forthe generated features, using the means and standard deviationscalculated from known normal data residuals for each respective knownhealth state, and wherein said set of normal residual reference patternsare likewise scaled.